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Enhance legal retrieval applications with an automatically induced knowledge base. Ka Kan Lo. Contents. Introduction Practice in legal retrieval Generation of Background concepts Combining concepts and contexts Conclusion. Introduction. Why needs advanced legal retrieval, e-discovery?
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Enhance legal retrieval applications with an automatically induced knowledge base Ka Kan Lo
Contents • Introduction • Practice in legal retrieval • Generation of Background concepts • Combining concepts and contexts • Conclusion
Introduction • Why needs advanced legal retrieval, e-discovery? • Document Collections • Legal Requirements • Efficiency
Introduction • What challenges? • Explosive growth of document size • Extensive document source • Expanding document format collection • Informal language
Introduction • Opportunities: • Background contexts utilization • Search documents deeply for every possible evidence • Examples – TREC: complaint as background information • More context information: Web and the links
Practice in Retrieval Process • TREC legal track practice: • Defendants devise queries • Plaintiffs’ turns • Final queries for production request • Document Retrieved
Practice in Retrieval Process • What can be added to the process? • Exploit the background information – complaints • Merge with the larger background – Web and links • Proposal in this work – Use Wikipedia as an example
Generation of Background concepts • Representation of Background concepts: • Entities & Relations • Ease the conversion from texts to concepts • Facilitate unsupervised operations
Generation of Background concepts • Concepts sources – Wikipedia • Page: a document • Title: central concept described by a document • Links: A set of concepts / terms to other pages • Word: Set of words
Generation of Background concepts • Facilitate lexical realization from texts to concepts: • Surface concepts: Mentioned by a page • Hidden concepts: Indexed by no pages but exist in pages
Generation of Background concepts • Entities: • Basic objects – named entities, locations, organizations …. • Definitions: • e⊂c, e≠r, e∈role of relations
Generation of Background concepts • Relations: • Relationships between concept • r⊂c, • r≠e, • r=<role1, role2, role3>, rolei = e
Semantical Domain • Semantical Domain: • Group of inter-related concepts, as defined by Wikipedians • Groups can be configured, reconfigured, depending on the size, nature of domains • Represent background information of different size, nature, structures
Semantical Domain • Operations: • D = {pagei} where pagei∈ E • Overlap • Subsumed • Join
Knowledge Extraction, Parsing • Parsing: • Conversion of syntactic parse into concepts representations • Dependency parsing • Fill the entities and relations automatically
Entities & Relations • Highlights of the process: • Syntactic parsing of sentences • Conversion from linguistic representation to concepts representation • Constraint the concept spaces by different sizes and scopes
Combining the concepts and background contexts • Algorithms: • Filter the background text and request text • Match the term set into Wikipedia • Build the network of concepts and relations • Combine for single network and filter unnecessary concepts • Extract terms and concepts and expand the query string • Fire the query to retrieval
Conclusion • Challenges in legal retrieval • Background contexts • Generation of background concepts • Project the context to concepts • Expand the queries for retrieval
Conclusion • Current work: • Integration of language learning (not only parsing) and concepts generation process • Large scale construction of networks with full document set in 3 languages on Grid: • English: 1.7 million • Spanish: 300 thousand • Chinese: 200 thousand
Conclusion • Current work: • Experiments running on 20M web pages corpus for expanded links • Generated Language, Concept spaces used in other Natural Language Technologies (NLT) • TREC-Legal: Testing the integration of knowledge base with the complaint text for queries • TREC-Legal: Building new matching mechanism (from KB induction) on small, concise set of documents
Thank you QA